CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer

  • Mark D. M. Leiserson
  • Hsin-Ta Wu
  • Fabio Vandin
  • Benjamin J. RaphaelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)


A major goal of large-scale cancer sequencing studies is to identify the genetic and epigenetic alterations that drive cancer development and to distinguish these events from random passenger mutations that have no consequence for cancer. Identifying driver mutations is a significant challenge due to the mutational heterogeneity of tumors: different combinations of somatic mutations drive different tumors, even those of the same cancer type.


Acute Myeloid Leukemia Gastric Adenocarcinoma Markov Chain Monte Carlo Algorithm Driver Mutation Mutual Exclusivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mark D. M. Leiserson
    • 1
  • Hsin-Ta Wu
    • 1
  • Fabio Vandin
    • 1
  • Benjamin J. Raphael
    • 1
    Email author
  1. 1.Department of Computer Science and Center for Computational Molecular BiologyBrown UniversityProvidenceUSA

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